Lesson 3: Your first machine learning model

Question 1

How do you find out a list of column headings of a DataFrame?

Answer 1

That is done with the columns property of the DataFrame (the bottom line of code below)

e.g. melbourne_data.columns

Question 2

How do you drop rows of data with missing values?

Answer 2

melbourne_data = melbourne_data.dropna(axis=0)

Question 3

The column we want to predict is called the p—– t—-

Answer 3

prediction target

Question 4

By convention, the prediction target is called -

Answer 4

y

Question 5

The columns that are inputted into our model (and later used to make predictions) are called f——

Answer 5

features

Question 6

By convention, the features data is called…

Answer 6

X

Question 7

How do you see a sample of the the first few rows of a DataFrame (“X”)?

Answer 7

X.head()

Question 8

S—-L—– is easily the most popular library for modeling the types of data typically stored in DataFrames

Answer 8

Scikit-learn

Question 9

Why define a number for random_state?

Answer 9

Specifying a number for random_state ensures you get the same results in each run.

Question 10

4 basic steps for building and using a model

Answer 10

1. Define: What type of model will it be? A decision tree? Some other type of model? Some other parameters of the model type are specified too.
2. Fit: Capture patterns from provided data. This is the heart of modeling.
3. Predict: Just what it sounds like
4. Evaluate: Determine how accurate the model’s predictions are.

Question 11

How do you import a Decision Tree model?

Answer 11

from sklearn.tree import DecisionTreeRegressor